Abstract
Existing deep learning-based finger-vein algorithms tend to use large-scale neural networks. From the perspective of computational complexity, this is not conducive to practical applications. Besides, in our opinion, finger-vein images often have relatively simple textures and are small in image size, it is not economical to use large-scale neural networks. Inspired by the increasing accuracy of lightweight neural networks on ImageNet, we introduce the lightweight neural network ShuffleNet V2 as a backbone to construct a basic pipeline for finger-vein verification. To customize the network for this application, we propose schemes to improve it from the aspects including data input, network structure, and loss function design. Experimental results on three public databases have exhibited the excellence of the proposed model.
Supported in part by Sino-Singapore International Joint Research Institute (No. 206-A017023, No. 206-A018001), Science and Technology Foundation of Guangzhou Huangpu Development District under Grant 201902010028, and NTU-PKU JRI.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Qiu, S., Liu, Y., Zhou, Y., Huang, J., Nie, Y.: Finger-vein recognition based on dual-sliding window localization and pseudo-elliptical transformer. Expert Syst. Appl. 64, 618–632 (2016)
Qin, H., El-Yacoubi, M.A.: Deep representation-based feature extraction and recovering for finger-vein verification. IEEE Trans. Inf. Forensics Secur. 12(8), 1816–1829 (2017)
Fang, Y., Wu, Q., Kang, W.: A novel finger-vein verification system based on two-stream convolutional network learning. Neurocomputing 290, 100–107 (2018)
Hu, H., Kang, W., Lu, Y., Fang, Y., Liu, H., Zhao, J., Deng, F.: FV-Net: learning a finger-vein feature representation based on a CNN. In: 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China, pp. 3489–3494. IEEE (2018)
Zeng, J., et al.: Finger vein verification algorithm based on fully convolutional neural network and conditional random field. IEEE Access 8, 65402–65419 (2020)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Hao, Z., Fang, P., Yang, H.: Finger vein recognition based on multi-task learning. In: Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence, Chengdu, China, pp. 133–140, ACM Digital (2020)
Ma, N., Zhang, X., Zheng, H.-T., Sun, J.: ShuffleNet V2: practical guidelines for efficient CNN architecture design. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. LNCS, vol. 11218, pp. 122–138. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01264-9_8
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, Nevada, USA, pp. 2818–2826, IEEE (2020)
Luo, H., Gu, Y., Liao, X., Lai, S., Jiang, W.: Bag of tricks and a strong baseline for deep person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA. IEEE (2019)
Yin, Y., Liu, L., Sun, X.: SDUMLA-HMT: a multimodal biometric database. In: Sun, Z., Lai, J., Chen, X., Tan, T. (eds.) CCBR 2011. LNCS, vol. 7098, pp. 260–268. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25449-9_33
Asaari, M.S.M., Suandi, S.A., Rosdi, B.A.: Fusion of band limited phase only correlation and width centroid contour distance for finger based biometrics. Expert Syst. Appl. 41(7), 3367–3382 (2014)
Lu, Y., Xie, S. J., Yoon, S., Wang, Z., Park, D. S.: An available database for the re-search of finger vein recognition. In: 2013 6th International Congress on Image and Signal Processing (CISP), Hangzhou, China, vol. 1, pp. 410–415, IEEE (2013)
Goyal, P., et al.: Accurate, large minibatch SGD: training ImageNet in 1 hour. arXiv preprint arXiv:1706.02677 (2017)
Zhong, Z., Zheng, L., Kang, G., Li, S., Yang, Y.: Random erasing data augmentation. arXiv preprint arXiv:1708.04896 (2017)
Wang, M., Tang, D.: Region of interest extraction for finger vein images with less information losses. Multimed. Tools Appl. 1–13 (2016). https://doi.org/10.1007/s11042-016-4285-2
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L. C.: Mobilenetv 2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, pp. 4510–4520, IEEE (2018)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, Nevada, USA. pp. 770–778, IEEE (2016)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K. Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Hawaii, USA, pp. 4700–4708, IEEE(2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Zheng, H., Hu, Y., Liu, B., Chen, G., Kot, A.C. (2020). A New Efficient Finger-Vein Verification Based on Lightweight Neural Network Using Multiple Schemes. In: Farkaš, I., Masulli, P., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2020. ICANN 2020. Lecture Notes in Computer Science(), vol 12396. Springer, Cham. https://doi.org/10.1007/978-3-030-61609-0_59
Download citation
DOI: https://doi.org/10.1007/978-3-030-61609-0_59
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-61608-3
Online ISBN: 978-3-030-61609-0
eBook Packages: Computer ScienceComputer Science (R0)